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<a href="#nested-classes">类</a> &#124;
<a href="#pub-types">Public 类型</a> &#124;
<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pro-methods">Protected 成员函数</a> &#124;
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<div class="title">pcl::OrganizedNeighborSearch&lt; PointT &gt; 模板类 参考</div>  </div>
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<p><b><a class="el" href="classpcl_1_1_organized_neighbor_search.html" title="OrganizedNeighborSearch class">OrganizedNeighborSearch</a></b> class  
 <a href="classpcl_1_1_organized_neighbor_search.html#details">更多...</a></p>

<p><code>#include &lt;<a class="el" href="organized__neighbor__search_8h_source.html">organized_neighbor_search.h</a>&gt;</code></p>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
类</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search_1_1nearest_neighbor_candidate.html">nearestNeighborCandidate</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight"><b><a class="el" href="classpcl_1_1_organized_neighbor_search_1_1nearest_neighbor_candidate.html" title="nearestNeighborCandidate entry for the nearest neighbor candidate queue">nearestNeighborCandidate</a></b> entry for the nearest neighbor candidate queue  <a href="classpcl_1_1_organized_neighbor_search_1_1nearest_neighbor_candidate.html#details">更多...</a><br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search_1_1radius_search_loopkup_entry.html">radiusSearchLoopkupEntry</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight"><b><a class="el" href="classpcl_1_1_organized_neighbor_search_1_1radius_search_loopkup_entry.html" title="radiusSearchLoopkupEntry entry for radius search lookup vector">radiusSearchLoopkupEntry</a></b> entry for radius search lookup vector  <a href="classpcl_1_1_organized_neighbor_search_1_1radius_search_loopkup_entry.html#details">更多...</a><br /></td></tr>
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typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloud</b></td></tr>
<tr class="separator:aa1bf698c1a6ef580f06bdcf9a33c476b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab529dda47b247453a484126a431c7548"><td class="memItemLeft" align="right" valign="top"><a id="ab529dda47b247453a484126a431c7548"></a>
typedef boost::shared_ptr&lt; <a class="el" href="classpcl_1_1_point_cloud.html">PointCloud</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudPtr</b></td></tr>
<tr class="separator:ab529dda47b247453a484126a431c7548"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae3d3d8c009fc0eb920cc183d7b4c434e"><td class="memItemLeft" align="right" valign="top"><a id="ae3d3d8c009fc0eb920cc183d7b4c434e"></a>
typedef boost::shared_ptr&lt; const <a class="el" href="classpcl_1_1_point_cloud.html">PointCloud</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudConstPtr</b></td></tr>
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Public 成员函数</h2></td></tr>
<tr class="memitem:a589ab5372bc7f62215963d8ac9121a6d"><td class="memItemLeft" align="right" valign="top"><a id="a589ab5372bc7f62215963d8ac9121a6d"></a>
&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#a589ab5372bc7f62215963d8ac9121a6d">OrganizedNeighborSearch</a> ()</td></tr>
<tr class="memdesc:a589ab5372bc7f62215963d8ac9121a6d"><td class="mdescLeft">&#160;</td><td class="mdescRight"><a class="el" href="classpcl_1_1_organized_neighbor_search.html" title="OrganizedNeighborSearch class">OrganizedNeighborSearch</a> constructor. <br /></td></tr>
<tr class="separator:a589ab5372bc7f62215963d8ac9121a6d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a23654f9c5afe770f181233d369ce5630"><td class="memItemLeft" align="right" valign="top"><a id="a23654f9c5afe770f181233d369ce5630"></a>
virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#a23654f9c5afe770f181233d369ce5630">~OrganizedNeighborSearch</a> ()</td></tr>
<tr class="memdesc:a23654f9c5afe770f181233d369ce5630"><td class="mdescLeft">&#160;</td><td class="mdescRight">Empty deconstructor. <br /></td></tr>
<tr class="separator:a23654f9c5afe770f181233d369ce5630"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:adc77da508e5523307f31177b7a57b1f4"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#adc77da508e5523307f31177b7a57b1f4">setInputCloud</a> (const PointCloudConstPtr &amp;cloud_arg)</td></tr>
<tr class="memdesc:adc77da508e5523307f31177b7a57b1f4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the input data set.  <a href="classpcl_1_1_organized_neighbor_search.html#adc77da508e5523307f31177b7a57b1f4">更多...</a><br /></td></tr>
<tr class="separator:adc77da508e5523307f31177b7a57b1f4"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1222cdf2d1d971ac2d0eae32e7d2afbf"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#a1222cdf2d1d971ac2d0eae32e7d2afbf">radiusSearch</a> (const PointCloudConstPtr &amp;cloud_arg, int index_arg, double radius_arg, std::vector&lt; int &gt; &amp;k_indices_arg, std::vector&lt; float &gt; &amp;k_sqr_distances_arg, int max_nn_arg=INT_MAX)</td></tr>
<tr class="memdesc:a1222cdf2d1d971ac2d0eae32e7d2afbf"><td class="mdescLeft">&#160;</td><td class="mdescRight">Search for all neighbors of query point that are within a given radius.  <a href="classpcl_1_1_organized_neighbor_search.html#a1222cdf2d1d971ac2d0eae32e7d2afbf">更多...</a><br /></td></tr>
<tr class="separator:a1222cdf2d1d971ac2d0eae32e7d2afbf"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a06ae9a29bf5b157b144652f781f8d5c8"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#a06ae9a29bf5b157b144652f781f8d5c8">radiusSearch</a> (int index_arg, const double radius_arg, std::vector&lt; int &gt; &amp;k_indices_arg, std::vector&lt; float &gt; &amp;k_sqr_distances_arg, int max_nn_arg=INT_MAX) const</td></tr>
<tr class="memdesc:a06ae9a29bf5b157b144652f781f8d5c8"><td class="mdescLeft">&#160;</td><td class="mdescRight">Search for all neighbors of query point that are within a given radius.  <a href="classpcl_1_1_organized_neighbor_search.html#a06ae9a29bf5b157b144652f781f8d5c8">更多...</a><br /></td></tr>
<tr class="separator:a06ae9a29bf5b157b144652f781f8d5c8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ade3b8b128fe1ccfdd0ca89baf41b4b30"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#ade3b8b128fe1ccfdd0ca89baf41b4b30">radiusSearch</a> (const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;p_q_arg, const double radius_arg, std::vector&lt; int &gt; &amp;k_indices_arg, std::vector&lt; float &gt; &amp;k_sqr_distances_arg, int max_nn_arg=INT_MAX) const</td></tr>
<tr class="memdesc:ade3b8b128fe1ccfdd0ca89baf41b4b30"><td class="mdescLeft">&#160;</td><td class="mdescRight">Search for all neighbors of query point that are within a given radius.  <a href="classpcl_1_1_organized_neighbor_search.html#ade3b8b128fe1ccfdd0ca89baf41b4b30">更多...</a><br /></td></tr>
<tr class="separator:ade3b8b128fe1ccfdd0ca89baf41b4b30"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3c18f38a4aad5fe6c05179906faf14cb"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#a3c18f38a4aad5fe6c05179906faf14cb">nearestKSearch</a> (const PointCloudConstPtr &amp;cloud_arg, int index_arg, int k_arg, std::vector&lt; int &gt; &amp;k_indices_arg, std::vector&lt; float &gt; &amp;k_sqr_distances_arg)</td></tr>
<tr class="memdesc:a3c18f38a4aad5fe6c05179906faf14cb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Search for k-nearest neighbors at the query point.  <a href="classpcl_1_1_organized_neighbor_search.html#a3c18f38a4aad5fe6c05179906faf14cb">更多...</a><br /></td></tr>
<tr class="separator:a3c18f38a4aad5fe6c05179906faf14cb"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:adcee6032ee08b2b104844b5171d7290e"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#adcee6032ee08b2b104844b5171d7290e">nearestKSearch</a> (int index_arg, int k_arg, std::vector&lt; int &gt; &amp;k_indices_arg, std::vector&lt; float &gt; &amp;k_sqr_distances_arg)</td></tr>
<tr class="memdesc:adcee6032ee08b2b104844b5171d7290e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Search for k-nearest neighbors at query point  <a href="classpcl_1_1_organized_neighbor_search.html#adcee6032ee08b2b104844b5171d7290e">更多...</a><br /></td></tr>
<tr class="separator:adcee6032ee08b2b104844b5171d7290e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af62665e1650116663a88042b657fa4c4"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#af62665e1650116663a88042b657fa4c4">nearestKSearch</a> (const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;p_q_arg, int k_arg, std::vector&lt; int &gt; &amp;k_indices_arg, std::vector&lt; float &gt; &amp;k_sqr_distances_arg)</td></tr>
<tr class="memdesc:af62665e1650116663a88042b657fa4c4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Search for k-nearest neighbors at given query point.  <a href="classpcl_1_1_organized_neighbor_search.html#af62665e1650116663a88042b657fa4c4">更多...</a><br /></td></tr>
<tr class="separator:af62665e1650116663a88042b657fa4c4"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a5abfa1075ea76fe6d9a3ebf6a1657518"><td class="memItemLeft" align="right" valign="top"><a id="a5abfa1075ea76fe6d9a3ebf6a1657518"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#a5abfa1075ea76fe6d9a3ebf6a1657518">getMaxDistance</a> () const</td></tr>
<tr class="memdesc:a5abfa1075ea76fe6d9a3ebf6a1657518"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the maximum allowed distance between the query point and its nearest neighbors. <br /></td></tr>
<tr class="separator:a5abfa1075ea76fe6d9a3ebf6a1657518"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3ed4f2135493ed0eb717429c0ac633ca"><td class="memItemLeft" align="right" valign="top"><a id="a3ed4f2135493ed0eb717429c0ac633ca"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#a3ed4f2135493ed0eb717429c0ac633ca">setMaxDistance</a> (double max_dist)</td></tr>
<tr class="memdesc:a3ed4f2135493ed0eb717429c0ac633ca"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the maximum allowed distance between the query point and its nearest neighbors. <br /></td></tr>
<tr class="separator:a3ed4f2135493ed0eb717429c0ac633ca"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-methods"></a>
Protected 成员函数</h2></td></tr>
<tr class="memitem:aeade2b5cd63cb8912b9b6eecf70ef2d7"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#aeade2b5cd63cb8912b9b6eecf70ef2d7">getPointByIndex</a> (const unsigned int index_arg) const</td></tr>
<tr class="memdesc:aeade2b5cd63cb8912b9b6eecf70ef2d7"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get point at index from input pointcloud dataset  <a href="classpcl_1_1_organized_neighbor_search.html#aeade2b5cd63cb8912b9b6eecf70ef2d7">更多...</a><br /></td></tr>
<tr class="separator:aeade2b5cd63cb8912b9b6eecf70ef2d7"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad07ad068f472e224feeaa1dd66987d45"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#ad07ad068f472e224feeaa1dd66987d45">generateRadiusLookupTable</a> (unsigned int width, unsigned int height)</td></tr>
<tr class="memdesc:ad07ad068f472e224feeaa1dd66987d45"><td class="mdescLeft">&#160;</td><td class="mdescRight">Generate radius lookup table. It is used to subsequentially iterate over points which are close to the search point  <a href="classpcl_1_1_organized_neighbor_search.html#ad07ad068f472e224feeaa1dd66987d45">更多...</a><br /></td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>pointPlaneProjection</b> (const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;point, int &amp;xpos, int &amp;ypos) const</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>getProjectedRadiusSearchBox</b> (const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;point_arg, double squared_radius_arg, int &amp;minX_arg, int &amp;minY_arg, int &amp;maxX_arg, int &amp;maxY_arg) const</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#a4ae57825bf5de9592691a43ac0ba1694">estimateFocalLengthFromInputCloud</a> ()</td></tr>
<tr class="memdesc:a4ae57825bf5de9592691a43ac0ba1694"><td class="mdescLeft">&#160;</td><td class="mdescRight">Estimate focal length parameter that was used during point cloud generation <br /></td></tr>
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<tr class="memitem:a6e280a00b93febd4afa5296f8d32a4fa"><td class="memItemLeft" align="right" valign="top"><a id="a6e280a00b93febd4afa5296f8d32a4fa"></a>
virtual std::string&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#a6e280a00b93febd4afa5296f8d32a4fa">getName</a> () const</td></tr>
<tr class="memdesc:a6e280a00b93febd4afa5296f8d32a4fa"><td class="mdescLeft">&#160;</td><td class="mdescRight">Class getName method. <br /></td></tr>
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Protected 属性</h2></td></tr>
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PointCloudConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a></td></tr>
<tr class="memdesc:a04f33565203b2bfe3133d1ae931776ff"><td class="mdescLeft">&#160;</td><td class="mdescRight">Pointer to input point cloud dataset. <br /></td></tr>
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<tr class="memitem:a883b34b66ae54cf081a29ca9cd4e58f1"><td class="memItemLeft" align="right" valign="top"><a id="a883b34b66ae54cf081a29ca9cd4e58f1"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#a883b34b66ae54cf081a29ca9cd4e58f1">max_distance_</a></td></tr>
<tr class="memdesc:a883b34b66ae54cf081a29ca9cd4e58f1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Maximum allowed distance between the query point and its k-neighbors. <br /></td></tr>
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double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#acdf755b364dcfd8ce27f1d4b04ae3792">focalLength_</a></td></tr>
<tr class="memdesc:acdf755b364dcfd8ce27f1d4b04ae3792"><td class="mdescLeft">&#160;</td><td class="mdescRight">Global focal length parameter <br /></td></tr>
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<tr class="memitem:a09469e7eff27436fdba9b751b593eb92"><td class="memItemLeft" align="right" valign="top"><a id="a09469e7eff27436fdba9b751b593eb92"></a>
std::vector&lt; <a class="el" href="classpcl_1_1_organized_neighbor_search_1_1radius_search_loopkup_entry.html">radiusSearchLoopkupEntry</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_organized_neighbor_search.html#a09469e7eff27436fdba9b751b593eb92">radiusSearchLookup_</a></td></tr>
<tr class="memdesc:a09469e7eff27436fdba9b751b593eb92"><td class="mdescLeft">&#160;</td><td class="mdescRight">Precalculated radius search lookup vector <br /></td></tr>
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<tr class="memitem:aca58961b91aa0a8eaf5745e24ba08b9a"><td class="memItemLeft" align="right" valign="top"><a id="aca58961b91aa0a8eaf5745e24ba08b9a"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><b>radiusLookupTableWidth_</b></td></tr>
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<tr class="memitem:aa4cbc914179a37f58104e6237a024b96"><td class="memItemLeft" align="right" valign="top"><a id="aa4cbc914179a37f58104e6237a024b96"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><b>radiusLookupTableHeight_</b></td></tr>
<tr class="separator:aa4cbc914179a37f58104e6237a024b96"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><h3>template&lt;typename PointT&gt;<br />
class pcl::OrganizedNeighborSearch&lt; PointT &gt;</h3>

<p><b><a class="el" href="classpcl_1_1_organized_neighbor_search.html" title="OrganizedNeighborSearch class">OrganizedNeighborSearch</a></b> class </p>
<dl class="section note"><dt>注解</dt><dd>This class provides neighbor search routines for organized point clouds. </dd>
<dd>
</dd>
<dd>
typename: PointT: type of point used in pointcloud </dd></dl>
<dl class="section author"><dt>作者</dt><dd>Julius Kammerl (<a href="#" onclick="location.href='mai'+'lto:'+'jul'+'iu'+'s@k'+'am'+'mer'+'l.'+'de'; return false;">juliu<span style="display: none;">.nosp@m.</span>s@ka<span style="display: none;">.nosp@m.</span>mmerl<span style="display: none;">.nosp@m.</span>.de</a>) </dd></dl>
</div><h2 class="groupheader">成员函数说明</h2>
<a id="ad07ad068f472e224feeaa1dd66987d45"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ad07ad068f472e224feeaa1dd66987d45">&#9670;&nbsp;</a></span>generateRadiusLookupTable()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_organized_neighbor_search.html">pcl::OrganizedNeighborSearch</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::generateRadiusLookupTable </td>
          <td>(</td>
          <td class="paramtype">unsigned int&#160;</td>
          <td class="paramname"><em>width</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">unsigned int&#160;</td>
          <td class="paramname"><em>height</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">protected</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Generate radius lookup table. It is used to subsequentially iterate over points which are close to the search point </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">width</td><td>of organized point cloud </td></tr>
    <tr><td class="paramname">height</td><td>of organized point cloud </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;    {</div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;      <span class="keywordtype">int</span> x, y, c;</div>
<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160; </div>
<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;      <span class="keywordflow">if</span> ( (this-&gt;radiusLookupTableWidth_!=(<span class="keywordtype">int</span>)width) || (this-&gt;radiusLookupTableHeight_!=(<span class="keywordtype">int</span>)height) )</div>
<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;      {</div>
<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160; </div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;        this-&gt;radiusLookupTableWidth_ = (int)width;</div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;        this-&gt;radiusLookupTableHeight_= (int)height;</div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160; </div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;        <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a09469e7eff27436fdba9b751b593eb92">radiusSearchLookup_</a>.clear ();</div>
<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;        <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a09469e7eff27436fdba9b751b593eb92">radiusSearchLookup_</a>.resize ((2*width+1) * (2*height+1));</div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160; </div>
<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;        c = 0;</div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;        <span class="keywordflow">for</span> (x = -(<span class="keywordtype">int</span>)width; x &lt; (int)width+1; x++)</div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;          <span class="keywordflow">for</span> (y = -(<span class="keywordtype">int</span>)height; y &lt;(int)height+1; y++)</div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;          {</div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;            <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a09469e7eff27436fdba9b751b593eb92">radiusSearchLookup_</a>[c++].defineShiftedSearchPoint(x, y);</div>
<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;          }</div>
<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160; </div>
<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;        std::sort (<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a09469e7eff27436fdba9b751b593eb92">radiusSearchLookup_</a>.begin (), <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a09469e7eff27436fdba9b751b593eb92">radiusSearchLookup_</a>.end ());</div>
<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;      }</div>
<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160; </div>
<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;    }</div>
<div class="ttc" id="aclasspcl_1_1_organized_neighbor_search_html_a09469e7eff27436fdba9b751b593eb92"><div class="ttname"><a href="classpcl_1_1_organized_neighbor_search.html#a09469e7eff27436fdba9b751b593eb92">pcl::OrganizedNeighborSearch::radiusSearchLookup_</a></div><div class="ttdeci">std::vector&lt; radiusSearchLoopkupEntry &gt; radiusSearchLookup_</div><div class="ttdoc">Precalculated radius search lookup vector</div><div class="ttdef"><b>Definition:</b> organized_neighbor_search.h:333</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#aeade2b5cd63cb8912b9b6eecf70ef2d7">&#9670;&nbsp;</a></span>getPointByIndex()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp; <a class="el" href="classpcl_1_1_organized_neighbor_search.html">pcl::OrganizedNeighborSearch</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::getPointByIndex </td>
          <td>(</td>
          <td class="paramtype">const unsigned int&#160;</td>
          <td class="paramname"><em>index_arg</em></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">protected</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Get point at index from input pointcloud dataset </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">index_arg</td><td>index representing the point in the dataset given by <em>setInputCloud</em> </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>PointT from input pointcloud dataset </dd></dl>
<div class="fragment"><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;    {</div>
<div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;      <span class="comment">// retrieve point from input cloud</span></div>
<div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;      assert (index_arg &lt; (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>)<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;points.size ());</div>
<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;      <span class="keywordflow">return</span> this-&gt;<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;points[index_arg];</div>
<div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160; </div>
<div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;    }</div>
<div class="ttc" id="aclasspcl_1_1_organized_neighbor_search_html_a04f33565203b2bfe3133d1ae931776ff"><div class="ttname"><a href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">pcl::OrganizedNeighborSearch::input_</a></div><div class="ttdeci">PointCloudConstPtr input_</div><div class="ttdoc">Pointer to input point cloud dataset.</div><div class="ttdef"><b>Definition:</b> organized_neighbor_search.h:324</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a3c18f38a4aad5fe6c05179906faf14cb">&#9670;&nbsp;</a></span>nearestKSearch() <span class="overload">[1/3]</span></h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
      <table class="memname">
        <tr>
          <td class="memname">int <a class="el" href="classpcl_1_1_organized_neighbor_search.html">pcl::OrganizedNeighborSearch</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::nearestKSearch </td>
          <td>(</td>
          <td class="paramtype">const PointCloudConstPtr &amp;&#160;</td>
          <td class="paramname"><em>cloud_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>index_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>k_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_indices_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_sqr_distances_arg</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Search for k-nearest neighbors at the query point. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">cloud_arg</td><td>the point cloud data </td></tr>
    <tr><td class="paramname">index_arg</td><td>the index in <em>cloud</em> representing the query point </td></tr>
    <tr><td class="paramname">k_arg</td><td>the number of neighbors to search for </td></tr>
    <tr><td class="paramname">k_indices_arg</td><td>the resultant indices of the neighboring points (must be resized to <em>k</em> a priori!) </td></tr>
    <tr><td class="paramname">k_sqr_distances_arg</td><td>the resultant squared distances to the neighboring points (must be resized to <em>k</em> a priori!) </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>number of neighbors found </dd></dl>
<div class="fragment"><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;    {</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;      this-&gt;<a class="code" href="classpcl_1_1_organized_neighbor_search.html#adc77da508e5523307f31177b7a57b1f4">setInputCloud</a> (cloud_arg);</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160; </div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;      <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a3c18f38a4aad5fe6c05179906faf14cb">nearestKSearch</a> (index_arg, k_arg, k_indices_arg, k_sqr_distances_arg);</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    }</div>
<div class="ttc" id="aclasspcl_1_1_organized_neighbor_search_html_a3c18f38a4aad5fe6c05179906faf14cb"><div class="ttname"><a href="classpcl_1_1_organized_neighbor_search.html#a3c18f38a4aad5fe6c05179906faf14cb">pcl::OrganizedNeighborSearch::nearestKSearch</a></div><div class="ttdeci">int nearestKSearch(const PointCloudConstPtr &amp;cloud_arg, int index_arg, int k_arg, std::vector&lt; int &gt; &amp;k_indices_arg, std::vector&lt; float &gt; &amp;k_sqr_distances_arg)</div><div class="ttdoc">Search for k-nearest neighbors at the query point.</div><div class="ttdef"><b>Definition:</b> organized_neighbor_search.hpp:155</div></div>
<div class="ttc" id="aclasspcl_1_1_organized_neighbor_search_html_adc77da508e5523307f31177b7a57b1f4"><div class="ttname"><a href="classpcl_1_1_organized_neighbor_search.html#adc77da508e5523307f31177b7a57b1f4">pcl::OrganizedNeighborSearch::setInputCloud</a></div><div class="ttdeci">void setInputCloud(const PointCloudConstPtr &amp;cloud_arg)</div><div class="ttdoc">Provide a pointer to the input data set.</div><div class="ttdef"><b>Definition:</b> organized_neighbor_search.h:91</div></div>
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<a id="af62665e1650116663a88042b657fa4c4"></a>
<h2 class="memtitle"><span class="permalink"><a href="#af62665e1650116663a88042b657fa4c4">&#9670;&nbsp;</a></span>nearestKSearch() <span class="overload">[2/3]</span></h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
      <table class="memname">
        <tr>
          <td class="memname">int <a class="el" href="classpcl_1_1_organized_neighbor_search.html">pcl::OrganizedNeighborSearch</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::nearestKSearch </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;&#160;</td>
          <td class="paramname"><em>p_q_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>k_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_indices_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_sqr_distances_arg</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Search for k-nearest neighbors at given query point. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">p_q_arg</td><td>the given query point </td></tr>
    <tr><td class="paramname">k_arg</td><td>the number of neighbors to search for </td></tr>
    <tr><td class="paramname">k_indices_arg</td><td>the resultant indices of the neighboring points (must be resized to k a priori!) </td></tr>
    <tr><td class="paramname">k_sqr_distances_arg</td><td>the resultant squared distances to the neighboring points (must be resized to k a priori!) </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>number of neighbors found </dd></dl>
<div class="fragment"><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;    {</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;      <span class="keywordtype">int</span> x_pos, y_pos, x, y, idx;</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;      std::size_t i;</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160; </div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;      <span class="keywordtype">int</span> leftX, rightX, leftY, rightY;</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160; </div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;      <span class="keywordtype">int</span> radiusSearchPointCount;</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160; </div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;      <span class="keywordtype">int</span> maxSearchDistance;</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;      <span class="keywordtype">double</span> squaredMaxSearchRadius;</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160; </div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;      assert (k_arg&gt;0);</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160; </div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;height == 1)</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;      {</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;        PCL_ERROR (<span class="stringliteral">&quot;[pcl::%s::nearestKSearch] Input dataset is not organized!&quot;</span>, <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a6e280a00b93febd4afa5296f8d32a4fa">getName</a> ().c_str ());</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;        <span class="keywordflow">return</span> 0;</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;      }</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160; </div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;      squaredMaxSearchRadius = <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a883b34b66ae54cf081a29ca9cd4e58f1">max_distance_</a>*<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a883b34b66ae54cf081a29ca9cd4e58f1">max_distance_</a>;</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160; </div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;      <span class="comment">// vector for nearest neighbor candidates</span></div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;      std::vector&lt;nearestNeighborCandidate&gt; nearestNeighbors;</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160; </div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;      <span class="comment">// iterator for radius seach lookup table</span></div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;      <span class="keyword">typename</span> std::vector&lt;radiusSearchLoopkupEntry&gt;::const_iterator radiusSearchLookup_Iterator;</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;      radiusSearchLookup_Iterator = <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a09469e7eff27436fdba9b751b593eb92">radiusSearchLookup_</a>.begin ();</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160; </div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;      nearestNeighbors.reserve (k_arg * 2);</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160; </div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;      <span class="comment">// project search point to plane</span></div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;      pointPlaneProjection (p_q_arg, x_pos, y_pos);</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;      x_pos += (int)<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;width/2;</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;      y_pos += (<span class="keywordtype">int</span>)<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;height/2;</div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160; </div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;      <span class="comment">// initialize result vectors</span></div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;      k_indices_arg.clear ();</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;      k_sqr_distances_arg.clear ();</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160; </div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160; </div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;      radiusSearchPointCount = 0;</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;      <span class="comment">// search for k_arg nearest neighbor candidates using the radius lookup table</span></div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;      <span class="keywordflow">while</span> ((radiusSearchLookup_Iterator != <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a09469e7eff27436fdba9b751b593eb92">radiusSearchLookup_</a>.end ()) &amp;&amp; ((int)nearestNeighbors.size () &lt; k_arg))</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;      {</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;        <span class="comment">// select point from organized pointcloud</span></div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;        x = x_pos + (*radiusSearchLookup_Iterator).x_diff_;</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;        y = y_pos + (*radiusSearchLookup_Iterator).y_diff_;</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;        radiusSearchLookup_Iterator++;</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;        radiusSearchPointCount++;</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160; </div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;        <span class="keywordflow">if</span> ((x &gt;= 0) &amp;&amp; (y &gt;= 0) &amp;&amp; (x &lt; (<span class="keywordtype">int</span>)<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;width) &amp;&amp; (y &lt; (<span class="keywordtype">int</span>)<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;height))</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;        {</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;          idx = y * (int)<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;width + x;</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;          <span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a>&amp; point = <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;points[idx];</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160; </div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;          if ((point.x == point.x) &amp;&amp; <span class="comment">// check for NaNs</span></div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;              (point.y == point.y) &amp;&amp;</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;              (point.z == point.z))</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;          {</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;            <span class="keywordtype">double</span> squared_distance;</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160; </div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;            <span class="keyword">const</span> <span class="keywordtype">double</span> point_dist_x = point.x - p_q_arg.x;</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;            <span class="keyword">const</span> <span class="keywordtype">double</span> point_dist_y = point.y - p_q_arg.y;</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;            <span class="keyword">const</span> <span class="keywordtype">double</span> point_dist_z = point.z - p_q_arg.z;</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160; </div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;            <span class="comment">// calculate squared distance</span></div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;            squared_distance</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;                = (point_dist_x * point_dist_x + point_dist_y * point_dist_y + point_dist_z * point_dist_z);</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160; </div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;            <span class="keywordflow">if</span> (squared_distance &lt;= squaredMaxSearchRadius)</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;            {</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;              <span class="comment">// we have a new candidate -&gt; add it</span></div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;              nearestNeighborCandidate newCandidate;</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;              newCandidate.index_ = idx;</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;              newCandidate.squared_distance_ = squared_distance;</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160; </div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;              nearestNeighbors.push_back (newCandidate);</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;            }</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160; </div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;          }</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;        }</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;      }</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160; </div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;      <span class="comment">// sort candidate list</span></div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;      std::sort (nearestNeighbors.begin (), nearestNeighbors.end ());</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160; </div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;      <span class="comment">// we found k_arg candidates -&gt; do radius search</span></div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;      <span class="keywordflow">if</span> ((<span class="keywordtype">int</span>)nearestNeighbors.size () == k_arg)</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;      {</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;        <span class="keywordtype">double</span> squared_radius;</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pointCountRadiusSearch;</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pointCountCircleSearch;</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160; </div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;        squared_radius = std::min&lt;double&gt;(nearestNeighbors.back ().squared_distance_, squaredMaxSearchRadius);</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160; </div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;        this-&gt;getProjectedRadiusSearchBox(p_q_arg, squared_radius, leftX, rightX, leftY, rightY);</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160; </div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;        leftX *=leftX;</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;        rightX *= rightX;</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;        leftY *=leftY;</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;        rightY *= rightY;</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160; </div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;        pointCountRadiusSearch = (rightX-leftX)*(rightY-leftY);</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160; </div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;        <span class="comment">// search for maximum distance between search point to window borders in 2-D search window</span></div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;        maxSearchDistance = 0;</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;        maxSearchDistance = std::max&lt;int&gt; (maxSearchDistance, leftX + leftY);</div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;        maxSearchDistance = std::max&lt;int&gt; (maxSearchDistance, leftX + rightY);</div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;        maxSearchDistance = std::max&lt;int&gt; (maxSearchDistance, rightX + leftY);</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;        maxSearchDistance = std::max&lt;int&gt; (maxSearchDistance, rightX + rightY);</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160; </div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;        maxSearchDistance +=1;</div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;        maxSearchDistance *=maxSearchDistance;</div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160; </div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;        pointCountCircleSearch= (int)(PI*(<span class="keywordtype">double</span>)(maxSearchDistance*maxSearchDistance));</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160; </div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;        <span class="keywordflow">if</span> (1){<span class="comment">//(pointCountCircleSearch&lt;pointCountRadiusSearch) {</span></div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160; </div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;          <span class="comment">// check for nearest neighbors within window</span></div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;          <span class="keywordflow">while</span> ((radiusSearchLookup_Iterator != <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a09469e7eff27436fdba9b751b593eb92">radiusSearchLookup_</a>.end ())</div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;              &amp;&amp; ((*radiusSearchLookup_Iterator).squared_distance_ &lt;= maxSearchDistance))</div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;          {</div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;            <span class="comment">// select point from organized point cloud</span></div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;            x = x_pos + (*radiusSearchLookup_Iterator).x_diff_;</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;            y = y_pos + (*radiusSearchLookup_Iterator).y_diff_;</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;            radiusSearchLookup_Iterator++;</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160; </div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;            <span class="keywordflow">if</span> ((x &gt;= 0) &amp;&amp; (y &gt;= 0) &amp;&amp; (x &lt; (<span class="keywordtype">int</span>)<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;width) &amp;&amp; (y &lt; (<span class="keywordtype">int</span>)<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;height))</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;            {</div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;              idx = y * (int)<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;width + x;</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;              <span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a>&amp; point = <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;points[idx];</div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160; </div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;              if ((point.x == point.x) &amp;&amp; <span class="comment">// check for NaNs</span></div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;                  (point.y == point.y) &amp;&amp; (point.z == point.z))</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;              {</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;                <span class="keywordtype">double</span> squared_distance;</div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160; </div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;                <span class="keyword">const</span> <span class="keywordtype">double</span> point_dist_x = point.x - p_q_arg.x;</div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;                <span class="keyword">const</span> <span class="keywordtype">double</span> point_dist_y = point.y - p_q_arg.y;</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;                <span class="keyword">const</span> <span class="keywordtype">double</span> point_dist_z = point.z - p_q_arg.z;</div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160; </div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;                <span class="comment">// calculate squared distance</span></div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;                squared_distance = (point_dist_x * point_dist_x + point_dist_y * point_dist_y + point_dist_z</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;                    * point_dist_z);</div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160; </div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;                <span class="keywordflow">if</span> ( squared_distance&lt;=squared_radius )</div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;                {</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;                  <span class="comment">// add candidate</span></div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;                  nearestNeighborCandidate newCandidate;</div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;                  newCandidate.index_ = idx;</div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;                  newCandidate.squared_distance_ = squared_distance;</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160; </div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;                  nearestNeighbors.push_back (newCandidate);</div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;                }</div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;              }</div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;            }</div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;          }</div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;        } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;          std::vector&lt;int&gt; k_radius_indices;</div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;          std::vector&lt;float&gt; k_radius_distances;</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160; </div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;          nearestNeighbors.clear();</div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160; </div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;          k_radius_indices.reserve (k_arg*2);</div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;          k_radius_distances.reserve (k_arg*2);</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160; </div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;          <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a1222cdf2d1d971ac2d0eae32e7d2afbf">radiusSearch</a> (p_q_arg, sqrt(squared_radius),k_radius_indices , k_radius_distances);</div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160; </div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;          std::cout &lt;&lt; k_radius_indices.size () &lt;&lt;std::endl;</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160; </div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;          <span class="keywordflow">for</span> (i = 0; i &lt; k_radius_indices.size (); i++)</div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;          {</div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;            nearestNeighborCandidate newCandidate;</div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;            newCandidate.index_ = k_radius_indices[i];</div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;            newCandidate.squared_distance_ = k_radius_distances[i];</div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160; </div>
<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;            nearestNeighbors.push_back (newCandidate);</div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;          }</div>
<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160; </div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160; </div>
<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;        }</div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160; </div>
<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;        std::sort (nearestNeighbors.begin (), nearestNeighbors.end ());</div>
<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160; </div>
<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;        <span class="comment">// truncate sorted nearest neighbor vector if we found more than k_arg candidates</span></div>
<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;        <span class="keywordflow">if</span> (nearestNeighbors.size () &gt; (<span class="keywordtype">size_t</span>)k_arg)</div>
<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;        {</div>
<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;          nearestNeighbors.resize (k_arg);</div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;        }</div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160; </div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;      }</div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160; </div>
<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;      <span class="comment">// copy results from nearestNeighbors vector to separate indices and distance vector</span></div>
<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;      k_indices_arg.resize (nearestNeighbors.size ());</div>
<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;      k_sqr_distances_arg.resize (nearestNeighbors.size ());</div>
<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160; </div>
<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;      <span class="keywordflow">for</span> (i = 0; i &lt; nearestNeighbors.size (); i++)</div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;      {</div>
<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;        k_indices_arg[i] = nearestNeighbors[i].index_;</div>
<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;        k_sqr_distances_arg[i] = nearestNeighbors[i].squared_distance_;</div>
<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;      }</div>
<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160; </div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;      <span class="keywordflow">return</span> k_indices_arg.size ();</div>
<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160; </div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;    }</div>
<div class="ttc" id="aclasspcl_1_1_organized_neighbor_search_html_a1222cdf2d1d971ac2d0eae32e7d2afbf"><div class="ttname"><a href="classpcl_1_1_organized_neighbor_search.html#a1222cdf2d1d971ac2d0eae32e7d2afbf">pcl::OrganizedNeighborSearch::radiusSearch</a></div><div class="ttdeci">int radiusSearch(const PointCloudConstPtr &amp;cloud_arg, int index_arg, double radius_arg, std::vector&lt; int &gt; &amp;k_indices_arg, std::vector&lt; float &gt; &amp;k_sqr_distances_arg, int max_nn_arg=INT_MAX)</div><div class="ttdoc">Search for all neighbors of query point that are within a given radius.</div><div class="ttdef"><b>Definition:</b> organized_neighbor_search.hpp:14</div></div>
<div class="ttc" id="aclasspcl_1_1_organized_neighbor_search_html_a6e280a00b93febd4afa5296f8d32a4fa"><div class="ttname"><a href="classpcl_1_1_organized_neighbor_search.html#a6e280a00b93febd4afa5296f8d32a4fa">pcl::OrganizedNeighborSearch::getName</a></div><div class="ttdeci">virtual std::string getName() const</div><div class="ttdoc">Class getName method.</div><div class="ttdef"><b>Definition:</b> organized_neighbor_search.h:314</div></div>
<div class="ttc" id="aclasspcl_1_1_organized_neighbor_search_html_a883b34b66ae54cf081a29ca9cd4e58f1"><div class="ttname"><a href="classpcl_1_1_organized_neighbor_search.html#a883b34b66ae54cf081a29ca9cd4e58f1">pcl::OrganizedNeighborSearch::max_distance_</a></div><div class="ttdeci">double max_distance_</div><div class="ttdoc">Maximum allowed distance between the query point and its k-neighbors.</div><div class="ttdef"><b>Definition:</b> organized_neighbor_search.h:327</div></div>
<div class="ttc" id="astructpcl_1_1_point_x_y_z_r_g_b_a_html"><div class="ttname"><a href="structpcl_1_1_point_x_y_z_r_g_b_a.html">pcl::PointXYZRGBA</a></div><div class="ttdoc">A point structure representing Euclidean xyz coordinates, and the RGBA color.</div><div class="ttdef"><b>Definition:</b> point_types.hpp:540</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#adcee6032ee08b2b104844b5171d7290e">&#9670;&nbsp;</a></span>nearestKSearch() <span class="overload">[3/3]</span></h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
      <table class="memname">
        <tr>
          <td class="memname">int <a class="el" href="classpcl_1_1_organized_neighbor_search.html">pcl::OrganizedNeighborSearch</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::nearestKSearch </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>index_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>k_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_indices_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_sqr_distances_arg</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Search for k-nearest neighbors at query point </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">index_arg</td><td>index representing the query point in the dataset given by <em>setInputCloud</em>. If indices were given in setInputCloud, index will be the position in the indices vector. </td></tr>
    <tr><td class="paramname">k_arg</td><td>the number of neighbors to search for </td></tr>
    <tr><td class="paramname">k_indices_arg</td><td>the resultant indices of the neighboring points (must be resized to <em>k</em> a priori!) </td></tr>
    <tr><td class="paramname">k_sqr_distances_arg</td><td>the resultant squared distances to the neighboring points (must be resized to <em>k</em> a priori!) </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>number of neighbors found </dd></dl>
<div class="fragment"><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    {</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160; </div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;      <span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> searchPoint = <a class="code" href="classpcl_1_1_organized_neighbor_search.html#aeade2b5cd63cb8912b9b6eecf70ef2d7">getPointByIndex</a> (index_arg);</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160; </div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;      <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a3c18f38a4aad5fe6c05179906faf14cb">nearestKSearch</a> (searchPoint, k_arg, k_indices_arg, k_sqr_distances_arg);</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;    }</div>
<div class="ttc" id="aclasspcl_1_1_organized_neighbor_search_html_aeade2b5cd63cb8912b9b6eecf70ef2d7"><div class="ttname"><a href="classpcl_1_1_organized_neighbor_search.html#aeade2b5cd63cb8912b9b6eecf70ef2d7">pcl::OrganizedNeighborSearch::getPointByIndex</a></div><div class="ttdeci">const PointT &amp; getPointByIndex(const unsigned int index_arg) const</div><div class="ttdoc">Get point at index from input pointcloud dataset</div><div class="ttdef"><b>Definition:</b> organized_neighbor_search.hpp:439</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a1222cdf2d1d971ac2d0eae32e7d2afbf">&#9670;&nbsp;</a></span>radiusSearch() <span class="overload">[1/3]</span></h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
      <table class="memname">
        <tr>
          <td class="memname">int <a class="el" href="classpcl_1_1_organized_neighbor_search.html">pcl::OrganizedNeighborSearch</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::radiusSearch </td>
          <td>(</td>
          <td class="paramtype">const PointCloudConstPtr &amp;&#160;</td>
          <td class="paramname"><em>cloud_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>index_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">double&#160;</td>
          <td class="paramname"><em>radius_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_indices_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_sqr_distances_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>max_nn_arg</em> = <code>INT_MAX</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Search for all neighbors of query point that are within a given radius. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">cloud_arg</td><td>the point cloud data </td></tr>
    <tr><td class="paramname">index_arg</td><td>the index in <em>cloud</em> representing the query point </td></tr>
    <tr><td class="paramname">radius_arg</td><td>the radius of the sphere bounding all of p_q's neighbors </td></tr>
    <tr><td class="paramname">k_indices_arg</td><td>the resultant indices of the neighboring points </td></tr>
    <tr><td class="paramname">k_sqr_distances_arg</td><td>the resultant squared distances to the neighboring points </td></tr>
    <tr><td class="paramname">max_nn_arg</td><td>if given, bounds the maximum returned neighbors to this value </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>number of neighbors found in radius </dd></dl>
<div class="fragment"><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;    {</div>
<div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;      this-&gt;<a class="code" href="classpcl_1_1_organized_neighbor_search.html#adc77da508e5523307f31177b7a57b1f4">setInputCloud</a> (cloud_arg);</div>
<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160; </div>
<div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;      <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a1222cdf2d1d971ac2d0eae32e7d2afbf">radiusSearch</a> (index_arg, radius_arg, k_indices_arg, k_sqr_distances_arg, max_nn_arg);</div>
<div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;    }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#ade3b8b128fe1ccfdd0ca89baf41b4b30">&#9670;&nbsp;</a></span>radiusSearch() <span class="overload">[2/3]</span></h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
      <table class="memname">
        <tr>
          <td class="memname">int <a class="el" href="classpcl_1_1_organized_neighbor_search.html">pcl::OrganizedNeighborSearch</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::radiusSearch </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;&#160;</td>
          <td class="paramname"><em>p_q_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const double&#160;</td>
          <td class="paramname"><em>radius_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_indices_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_sqr_distances_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>max_nn_arg</em> = <code>INT_MAX</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Search for all neighbors of query point that are within a given radius. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">p_q_arg</td><td>the given query point </td></tr>
    <tr><td class="paramname">radius_arg</td><td>the radius of the sphere bounding all of p_q's neighbors </td></tr>
    <tr><td class="paramname">k_indices_arg</td><td>the resultant indices of the neighboring points </td></tr>
    <tr><td class="paramname">k_sqr_distances_arg</td><td>the resultant squared distances to the neighboring points </td></tr>
    <tr><td class="paramname">max_nn_arg</td><td>if given, bounds the maximum returned neighbors to this value </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>number of neighbors found in radius </dd></dl>
<div class="fragment"><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;    {</div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;height == 1)</div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;      {</div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;        PCL_ERROR (<span class="stringliteral">&quot;[pcl::%s::radiusSearch] Input dataset is not organized!&quot;</span>, <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a6e280a00b93febd4afa5296f8d32a4fa">getName</a> ().c_str ());</div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;        <span class="keywordflow">return</span> 0;</div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;      }</div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160; </div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;      <span class="comment">// search window</span></div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;      <span class="keywordtype">int</span> leftX, rightX, leftY, rightY;</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;      <span class="keywordtype">int</span> x, y, idx;</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;      <span class="keywordtype">double</span> squared_distance, squared_radius;</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;      <span class="keywordtype">int</span> nnn;</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160; </div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;      k_indices_arg.clear ();</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;      k_sqr_distances_arg.clear ();</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160; </div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;      squared_radius = radius_arg*radius_arg;</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160; </div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;      this-&gt;getProjectedRadiusSearchBox(p_q_arg, squared_radius, leftX, rightX, leftY, rightY);</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160; </div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160; </div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160; </div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160; </div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;      <span class="comment">// iterate over all children</span></div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;      nnn = 0;</div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;      <span class="keywordflow">for</span> (x = leftX; (x &lt;= rightX) &amp;&amp; (nnn &lt; max_nn_arg); x++)</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;        <span class="keywordflow">for</span> (y = leftY; (y &lt;= rightY) &amp;&amp; (nnn &lt; max_nn_arg); y++)</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;        {</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;          idx = y * <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;width + x;</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;          <span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a>&amp; point = <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;points[idx];</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160; </div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">double</span> point_dist_x = point.x - p_q_arg.x;</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">double</span> point_dist_y = point.y - p_q_arg.y;</div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">double</span> point_dist_z = point.z - p_q_arg.z;</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160; </div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;          <span class="comment">// calculate squared distance</span></div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;          squared_distance = (point_dist_x * point_dist_x + point_dist_y * point_dist_y + point_dist_z * point_dist_z);</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160; </div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;          <span class="comment">// check distance and add to results</span></div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;          <span class="keywordflow">if</span> (squared_distance &lt;= squared_radius)</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;          {</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;            k_indices_arg.push_back (idx);</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;            k_sqr_distances_arg.push_back (squared_distance);</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;            nnn++;</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;          }</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;        }</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160; </div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;      <span class="keywordflow">return</span> nnn;</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160; </div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;    }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a06ae9a29bf5b157b144652f781f8d5c8">&#9670;&nbsp;</a></span>radiusSearch() <span class="overload">[3/3]</span></h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
      <table class="memname">
        <tr>
          <td class="memname">int <a class="el" href="classpcl_1_1_organized_neighbor_search.html">pcl::OrganizedNeighborSearch</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::radiusSearch </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>index_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const double&#160;</td>
          <td class="paramname"><em>radius_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_indices_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_sqr_distances_arg</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>max_nn_arg</em> = <code>INT_MAX</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Search for all neighbors of query point that are within a given radius. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">index_arg</td><td>index representing the query point in the dataset given by <em>setInputCloud</em>. If indices were given in setInputCloud, index will be the position in the indices vector </td></tr>
    <tr><td class="paramname">radius_arg</td><td>radius of the sphere bounding all of p_q's neighbors </td></tr>
    <tr><td class="paramname">k_indices_arg</td><td>the resultant indices of the neighboring points </td></tr>
    <tr><td class="paramname">k_sqr_distances_arg</td><td>the resultant squared distances to the neighboring points </td></tr>
    <tr><td class="paramname">max_nn_arg</td><td>if given, bounds the maximum returned neighbors to this value </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>number of neighbors found in radius </dd></dl>
<div class="fragment"><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;    {</div>
<div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160; </div>
<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;      <span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> searchPoint = <a class="code" href="classpcl_1_1_organized_neighbor_search.html#aeade2b5cd63cb8912b9b6eecf70ef2d7">getPointByIndex</a> (index_arg);</div>
<div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160; </div>
<div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;      <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a1222cdf2d1d971ac2d0eae32e7d2afbf">radiusSearch</a> (searchPoint, radius_arg, k_indices_arg, k_sqr_distances_arg, max_nn_arg);</div>
<div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160; </div>
<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;    }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#adc77da508e5523307f31177b7a57b1f4">&#9670;&nbsp;</a></span>setInputCloud()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_organized_neighbor_search.html">pcl::OrganizedNeighborSearch</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setInputCloud </td>
          <td>(</td>
          <td class="paramtype">const PointCloudConstPtr &amp;&#160;</td>
          <td class="paramname"><em>cloud_arg</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Provide a pointer to the input data set. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">cloud_arg</td><td>the const boost shared pointer to a <a class="el" href="classpcl_1_1_point_cloud.html" title="PointCloud represents the base class in PCL for storing collections of 3D points.">PointCloud</a> message </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;      {</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160; </div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;        <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a> != cloud_arg)</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;        {</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;          <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a> = cloud_arg;</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160; </div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;          <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a4ae57825bf5de9592691a43ac0ba1694">estimateFocalLengthFromInputCloud</a> ();</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;          <a class="code" href="classpcl_1_1_organized_neighbor_search.html#ad07ad068f472e224feeaa1dd66987d45">generateRadiusLookupTable</a> (<a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;width, <a class="code" href="classpcl_1_1_organized_neighbor_search.html#a04f33565203b2bfe3133d1ae931776ff">input_</a>-&gt;height);</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;        }</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_organized_neighbor_search_html_a4ae57825bf5de9592691a43ac0ba1694"><div class="ttname"><a href="classpcl_1_1_organized_neighbor_search.html#a4ae57825bf5de9592691a43ac0ba1694">pcl::OrganizedNeighborSearch::estimateFocalLengthFromInputCloud</a></div><div class="ttdeci">void estimateFocalLengthFromInputCloud()</div><div class="ttdoc">Estimate focal length parameter that was used during point cloud generation</div><div class="ttdef"><b>Definition:</b> organized_neighbor_search.hpp:378</div></div>
<div class="ttc" id="aclasspcl_1_1_organized_neighbor_search_html_ad07ad068f472e224feeaa1dd66987d45"><div class="ttname"><a href="classpcl_1_1_organized_neighbor_search.html#ad07ad068f472e224feeaa1dd66987d45">pcl::OrganizedNeighborSearch::generateRadiusLookupTable</a></div><div class="ttdeci">void generateRadiusLookupTable(unsigned int width, unsigned int height)</div><div class="ttdoc">Generate radius lookup table. It is used to subsequentially iterate over points which are close to th...</div><div class="ttdef"><b>Definition:</b> organized_neighbor_search.hpp:411</div></div>
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<hr/>该类的文档由以下文件生成:<ul>
<li>cuda/nn/<a class="el" href="organized__neighbor__search_8h_source.html">organized_neighbor_search.h</a></li>
<li>cuda/nn/<a class="el" href="organized__neighbor__search_8hpp_source.html">organized_neighbor_search.hpp</a></li>
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